Ground Control from Major Tom — Breaking Radio Silence: New Perspectives on Project Management

Since I began this blog I have used it as a means of testing out and sharing ideas about project management, information systems, as well to cover occasional thoughts about music, the arts, and the meaning of wisdom.

My latest hiatus from writing was due to the fact that I was otherwise engaged in a different sort of writing–tech writing–and in exploring some mathematical explorations related to my chosen vocation, aside from running a business and–you know–living life.  There are only so many hours in the day.  Furthermore, when one writes over time about any one topic it seems that one tends to repeat oneself.  I needed to break that cycle so that I could concentrate on bringing something new to the table.  After all, it is not as if this blog attracts a massive audience–and purposely so.  The topics on which I write are highly specialized and the members of the community that tend to follow this blog and send comments tend to be specialized as well.  I air out thoughts here that are sometimes only vaguely conceived so that they can be further refined.

Now that that is out of the way, radio silence is ending until, well, the next contemplation or massive workload that turns into radio silence.

Over the past couple of months I’ve done quite a bit of traveling, and so have some new perspectives that and trends that I noted and would like to share, and which will be the basis (in all likelihood) of future, more in depth posts.  But here is a list that I have compiled:

a.  The time of niche analytical “tools” as acceptable solutions among forward-leaning businesses and enterprises is quickly drawing to a close.  Instead, more comprehensive solutions that integrate data across domains are taking the market and disrupting even large players that have not adapted to this new reality.  The economics are too strong to stay with the status quo.  In the past the barrier to integration of more diverse and larger sets of data was the high cost of traditional BI with its armies of data engineers and analysts providing marginal value that did not always square with the cost.  Now virtually any data can be accessed and visualized.  The best solutions, providing pre-built domain knowledge for targeted verticals, are the best and will lead and win the day.

b.  Along these same lines, apps and services designed around the bureaucratic end-of-month chart submission process are running into the new paradigm among project management leaders that this cycle is inadequate, inefficient, and ineffective.  The incentives are changing to reward actual project management in lieu of project administration.  The core fallacy of apps that provide standard charts based solely on user’s perceptions of looking at data is that they assume that the PM domain knows what it needs to see.  The new paradigm is instead to provide a range of options based on the knowledge that can be derived from data.  Thus, while the options in the new solutions provide the standard charts and reports that have always informed management, KDD (knowledge discovery in database) principles are opening up new perspectives in understanding project dynamics and behavior.

c.  Earned value is *not* the nexus of Integrated Project Management (IPM).  I’m sure many of my colleagues in the community will find this statement to be provocative, only because it is what they are thinking but have been hesitant to voice.  A big part of their hesitation is that the methodology is always under attack by those who wish to avoid accountability for program performance.  Thus, let me make a point about Earned Value Management (EVM) for clarity–it is an essential methodology in assessing project performance and the probability of meeting the constraints of the project budget.  It also contributes data essential to project predictive analytics.  What the data shows from a series of DoD studies (currently sadly unpublished), however, is that it is planning (via a Integrated Master Plan) and scheduling (via an Integrated Master Schedule) that first ties together the essential elements of the project, and will record the baking in of risk within the project.  Risk manifested in poorly tying contract requirements, technical performance measures, and milestones to the plan, and then manifested in poor execution will first be recorded in schedule (time-based) performance.  This is especially true for firms that apply resource-loading in their schedules.  By the time this risk translates and is recorded in EVM metrics, the project management team is performing risk handling and mitigation to blunt the impact on the performance management baseline (the money).  So this still raises the question: what is IPM?  I have a few ideas and will share those in other posts.

d.  Along these lines, there is a need for a Schedule (IMS) Gold Card that provides the essential basis of measurement of programmatic risk during project execution.  I am currently constructing one with collaboration and will put out a few ideas.

e.  Finally, there is still room for a lot of improvement in project management.  For all of the gurus, methodologies, consultants, body shops, and tools that are out there, according to PMI, more than a third of projects fail to meet project goals, almost half to meet budget expectations, less than half finished on time, and almost half experienced scope creep, which, I suspect, probably caused “failure” to be redefined and under-reported in their figures.  The assessment for IT projects is also consistent with this report, with CIO.com reporting that more than half of IT projects fail in terms of meeting performance, cost, and schedule goals.  From my own experience and those of my colleagues, the need to solve the standard 20-30% slippage in schedule and similar overrun in costs is an old refrain.  So too is the frustration that it need take 23 years to deploy a new aircraft.  A .5 CPI and SPI (to use EVM terminology) is not an indicator of success.  What this indicates, instead, is that there need to be some adjustments and improvements in how we do business.  The first would be to adjust incentives to encourage and reward the identification of risk in project performance.  The second is to deploy solutions that effectively access and provide information to the project team that enable them to address risk.  As with all of the points noted in this post, I have some other ideas in this area that I will share in future posts.

Onward and upward.

Rear View Mirror — Correcting a Project Management Fallacy

“The past is never dead. It’s not even past.” —  William Faulkner, Requiem for a Nun

Over the years I and others have briefed project managers on project performance using KPPs, earned value management, schedule analysis, business analytics, and what we now call predictive analytics. Oftentimes, some set of figures will be critiqued as being ineffective or unhelpful; that the analytics “only look in the rear view mirror” and that they “tell me what I already know.”

In approaching this critique, it is useful to understand Faulkner’s oft-cited quote above.  When we walk down a street, let us say it is a busy city street in any community of good size, we are walking in the past.  The moment we experience something it is in the past.  If we note the present condition of our city street we will see that for every building, park, sidewalk, and individual that we pass on that sidewalk, each has a history.  These structures and the people are as much driven by their pasts as their expectations for the future.

Now let us take a snapshot of our street.  In doing so we can determine population density, ethnic demographics, property values, crime rate, and numerous other indices and parameters regarding what is there.  No doubt, if we stop here we are just “looking in the rear view mirror” and noting what we may or may not know, however certain our anecdotal filter.

Now, let us say that we have an affinity for this street and may want to live there.  We will take the present indices and parameters that noted above, which describe our geographical environment, and trend it.  We may find that housing pricing are rising or falling, that crime is rising or falling, etc.  If we delve into the street’s ownership history we may find that one individual or family possesses more than one structure, or that there is a great deal of diversity.  We may find that a Superfund site is not too far away.  We may find that economic demographics are pointing to stagnation of the local economy, or that the neighborhood is becoming gentrified.  Just by time-phasing and delving into history–by mapping out the trends and noting the significant historical background–provides us with enough information to inform us about whether our affinity is grounded in reality or practicality.

But let us say that, despite negatives, we feel that this is the next up-and-coming neighborhood.  We would need signs to make that determination.  For example, what kinds of businesses have moved into the neighborhood and what is their number?  What demographic do they target?  There are many other questions that can be asked to see if our economic analysis is valid–and that analysis would need to be informed by risk.

The fact of the matter is that we are always living with the past: the cumulative effect of the past actions of numerous individuals, including our own, and organizations, groups of individuals, and institutions; not to mention larger economic forces well beyond our control.  Any desired change in the trajectory of the system being evaluated must identify those elements that can be impacted or influenced, and an analysis of the effort that must be expended to bring about the change, is also essential.

This is a scientific fact, proven countless times by physics, biology, and other disciplines.  A deterministic universe, which provides for some uncertainty at any given point at our level of existence, drives the possible within very small limits of possibility and even smaller limits of probability.  What this means in plain language is that the future is usually a function of the past.

Any one number or index, no doubt, does not necessarily tell us something important.  But it could if it is relevant, material, and prompts further inquiry essential to project performance.

For example, let us look at an integrated master schedule that underlies a typical medium-sized project.

 

We will select a couple of metrics that indicates project schedule performance.  In the case below we are looking at task hits and misses and Baseline Execution Index, a popular index that determines efficiency in meeting baseline schedule planning.

Note that the chart above plots the performance over time.  What will it take to improve our efficiency?  So as a quick logic check on realism, let’s take a look at the work to date with all of the late starts and finishes.

Our bow waves track the cumulative effort to date.  As we work to clear missed starts or missed finishes in a project we also must devote resources to the accomplishment of current work that is still in line with the baseline.  What this means is that additional resources may need to be devoted to particular areas of work accomplishment or risk handling.

This is not, of course, the limit to our analysis that should be undertaken.  The point here is that at every point in history in every system we stand at a point of the cumulative efforts, risk, failure, success, and actions of everyone who came before us.  At the microeconomic level this is also true within our project management systems.  There are also external constraints and influences that will define the framing assumptions and range of possibilities and probabilities involved in project outcomes.

The shear magnitude of the bow waves that we face in all endeavors will often be too great to fully overcome.  As an analogy, a bow wave in complex systems is more akin to a tsunami as opposed to the tidal waves that crash along our shores.  All of the force of all of the collective actions that have preceded present time will drive our trajectory.

This is known as inertia.

Identifying and understanding the contributors to the inertia that is driving our performance is important to knowing what to do.  Thus, looking in the rear view mirror is important and not a valid argument for ignoring an inconvenient metric that may only require additional context.  Furthermore, knowing where we sit is important and not insignificant.  Knowing the factors that put us where we are–and the effort that it will take to influence our destiny–will guide what is possible and not possible in our future actions.

Note:  All charted data is notional and is not from an actual project.

Something New (Again)– Top Project Management Trends 2017

Atif Qureshi at Tasque, which I learned via Dave Gordon’s blog, went out to LinkedIn’s Project Management Community to ask for the latest tends in project management.  You can find the raw responses to his inquiry at his blog here.  What is interesting is that some of these latest trends are much like the old trends which, given continuity makes sense.  But it is instructive to summarize the ones that came up most often.  Note that while Mr. Qureshi was looking for ten trends, and taken together he definitely lists more than ten, there is a lot of overlap.  In total the major issues seem to the five areas listed below.

a.  Agile, its hybrids, and its practical application.

It should not surprise anyone that the latest buzzword is Agile.  But what exactly is it in its present incarnation?  There is a great deal of rising criticism, much of it valid, that it is a way for developers and software PMs to avoid accountability. Anyone ready Glen Alleman’s Herding Cat’s Blog is aware of the issues regarding #NoEstimates advocates.  As a result, there are a number hybrid implementations of Agile that has Agile purists howling and non-purists adapting as they always do.  From my observations, however, there is an Ur-Agile that is out there common to all good implementations and wrote about them previously in this blog back in 2015.  Given the time, I think it useful to repeat it here.

The best articulation of Agile that I have read recently comes from Neil Killick, whom I have expressed some disagreement on the #NoEstimates debate and the more cultish aspects of Agile in past posts, but who published an excellent post back in July (2015) entitled “12 questions to find out: Are you doing Agile Software Development?”

Here are Neil’s questions:

  1. Do you want to do Agile Software Development? Yes – go to 2. No – GOODBYE.
  2. Is your team regularly reflecting on how to improve? Yes – go to 3. No – regularly meet with your team to reflect on how to improve, go to 2.
  3. Can you deliver shippable software frequently, at least every 2 weeks? Yes – go to 4. No – remove impediments to delivering a shippable increment every 2 weeks, go to 3.
  4. Do you work daily with your customer? Yes – go to 5. No – start working daily with your customer, go to 4.
  5. Do you consistently satisfy your customer? Yes – go to 6. No – find out why your customer isn’t happy, fix it, go to 5.
  6. Do you feel motivated? Yes – go to 7. No – work for someone who trusts and supports you, go to 2.
  7. Do you talk with your team and stakeholders every day? Yes – go to 8. No – start talking with your team and stakeholders every day, go to 7.
  8. Do you primarily measure progress with working software? Yes – go to 9. No – start measuring progress with working software, go to 8.
  9. Can you maintain pace of development indefinitely? Yes – go to 10. No – take on fewer things in next iteration, go to 9.
  10. Are you paying continuous attention to technical excellence and good design? Yes – go to 11. No – start paying continuous attention to technical excellent and good design, go to 10.
  11. Are you keeping things simple and maximising the amount of work not done? Yes – go to 12. No – start keeping things simple and writing as little code as possible to satisfy the customer, go to 11.
  12. Is your team self-organising? Yes – YOU’RE DOING AGILE SOFTWARE DEVELOPMENT!! No – don’t assign tasks to people and let the team figure out together how best to satisfy the customer, go to 12.

Note that even in software development based on Agile you are still “provid(ing) value by independently developing IP based on customer requirements.”  Only you are doing it faster and more effectively.

With the possible exception of the “self-organizing” meme, I find that items through 11 are valid ways of identifying Agile.  Given that the list says nothing about establishing closed-loop analysis of progress says nothing about estimates or the need to monitor progress, especially on complex projects.  As a matter of fact one of the biggest impediments noted elsewhere in industry is the inability of Agile to scale.  This limitations exists in its most simplistic form because Agile is fine in the development of well-defined limited COTS applications and smartphone applications.  It doesn’t work so well when one is pushing technology while developing software, especially for a complex project involving hundreds of stakeholders.  One other note–the unmentioned emphasis in Agile is technical performance measurement, since progress is based on satisfying customer requirements.  TPM, when placed in the context of a world of limited resources, is the best measure of all.

b.  The integration of new technology into PM and how to upload the existing PM corporate knowledge into that technology.

This is two sides of the same coin.  There is always  debate about the introduction of new technologies within an organization and this debate places in stark contrast the differences between risk aversion and risk management.

Project managers, especially in the complex project management environment of aerospace & defense tend, in general, to be a hardy lot.  Consisting mostly of engineers they love to push the envelope on technology development.  But there is also a stripe of engineers among them that do not apply this same approach of measured risk to their project management and business analysis system.  When it comes to tracking progress, resource management, programmatic risk, and accountability they frequently enter the risk aversion mode–believing that the less eyes on what they do the more leeway they have in achieving the technical milestones.  No doubt this is true in a world of unlimited time and resources, but that is not the world in which we live.

Aside from sub-optimized self-interest, the seeds of risk aversion come from the fact that many of the disciplines developed around performance management originated in the financial management community, and many organizations still come at project management efforts from perspective of the CFO organization.  Such rice bowl mentality, however, works against both the project and the organization.

Much has been made of the wall of honor for those CIA officers that have given their lives for their country, which lies to the right of the Langley headquarters entrance.  What has not gotten as much publicity is the verse inscribed on the wall to the left:

“And ye shall know the truth and the truth shall make you free.”

      John VIII-XXXII

In many ways those of us in the project management community apply this creed to the best of our ability to our day-to-day jobs, and it lies as the basis for all of the management improvement from Deming’s concept of continuous process improvement, through the application of Six Sigma and other management improvement methods.  What is not part of this concept is that one will apply improvement only when a customer demands it, though they have asked politely for some time.  The more information we have about what is happening in our systems, the better the project manager and the project team is armed with applying the expertise which qualified the individuals for their jobs to begin with.

When it comes to continual process improvement one does not need to wait to apply those technologies that will improve project management systems.  As a senior management (and well-respected engineer) when I worked in Navy told me; “if my program managers are doing their job virtually every element should be in the yellow, for only then do I know that they are managing risk and pushing the technology.”

But there are some practical issues that all managers must consider when managing the risks in introducing new technology and determining how to bring that technology into existing business systems without completely disrupting the organization.  This takes–good project management practices that, for information systems, includes good initial systems analysis, identification of those small portions of the organization ripe for initial entry in piloting, and a plan of data normalization and rationalization so that corporate knowledge is not lost.  Adopting systems that support more open systems that militate against proprietary barriers also helps.

c.  The intersection of project management and business analysis and its effects.

As data becomes more transparent through methods of normalization and rationalization–and the focus shifts from “tools” to the knowledge that can be derived from data–the clear separation that delineated project management from business analysis in line-and-staff organization becomes further blurred.  Even within the project management discipline, the separation in categorization of schedule analysts from cost analysts from financial analyst are becoming impediments in fully exploiting the advantages in looking at all data that is captured and which affects project performance.

d.  The manner of handling Big Data, business intelligence, and analytics that result.

Software technologies are rapidly developing that break the barriers of self-contained applications that perform one or two focused operations or a highly restricted group of operations that provide functionality focused on a single or limited set of business processes through high level languages that are hard-coded.  These new technologies, as stated in the previous section, allow users to focus on access to data, making the interface between the user and the application highly adaptable and customizable.  As these technologies are deployed against larger datasets that allow for integration of data across traditional line-and-staff organizations, they will provide insight that will garner businesses competitive advantages and productivity gains against their contemporaries.  Because of these technologies, highly labor-intensive data mining and data engineering projects that were thought to be necessary to access Big Data will find themselves displaced as their cost and lack of agility is exposed.  Internal or contracted out custom software development devoted along these same lines will also be displaced just as COTS has displaced the high overhead associated with these efforts in other areas.  This is due to the fact that hardware and processes developments are constantly shifting the definition of “Big Data” to larger and larger datasets to the point where the term will soon have no practical meaning.

e.  The role of the SME given all of the above.

The result of the trends regarding technology will be to put the subject matter expert back into the driver’s seat.  Given adaptive technology and data–and a redefinition of the analyst’s role to a more expansive one–we will find that the ability to meet the needs of functionality and the user experience is almost immediate.  Thus, when it comes to business and project management systems, the role of Agile, while these developments reinforce the characteristics that I outlined above are made real, the weakness of its applicability to more complex and technical projects is also revealed.  It is technology that will reduce the risk associated with contract negotiation, processes, documentation, and planning.  Walking away from these necessary components to project management obfuscates and avoids the hard facts that oftentimes must be addressed.

One final item that Mr. Qureshi mentions in a follow-up post–and which I have seen elsewhere in similar forums–concerns operational security.  In deployment of new technologies a gatekeeper must be aware of whether that technology will not open the organization’s corporate knowledge to compromise.  Given the greater and more integrated information and knowledge garnered by new technology, as good managers it is incumbent to ensure these improvements do not translate into undermining the organization.

The (Contract) is parent to the (Project)

It’s been a late spring filled with travel and tragedy.  Blogging had taken a hiatus, except for AITS.org, which I highly encourage you check out.  My next item will be posted there the first week of July.  The news from Orlando is that we are united and strong as a community, facing down both crackpots and opportunists, and so it is back to work.

At a recent conference one of the more interesting conversations surrounded the difference between contract and project management.  To many people this is one of the same–and a simple Google search reinforces this perception–but, I think, this is a misconception.

The context of the discussion was interesting in that it occurred during an earned value management-focused event.  EVM pitches itself as the glue that binds together the parts of project management that further constitutes integrated project management, but I respectfully disagree.  If we ignore the self-promotion of this position and like good engineers stick to our empiricist approach, we will find that EVM is a method of deriving the financial value of effort within a project.  It is also a pretty good indicator of cost risk manifestation.  This last shouldn’t be taken too far.

A recent DoD study, which is not yet published, demonstrated that early warning cannot be had by EVM even when diving into the details.  Instead, ensuring integration and traceability to the work package level tied to schedule activities could be traced to the slips in schedule (and the associated impact of the bow wave) against the integrated master schedule (IMS), which then served as the window to early warning.  So within the limited context of project performance, EVM itself is just one of many points of entry to eventually get to the answer.  This answer, of course, needs to be both timely and material.

Material in this case refers to the ability to understand the relevance and impact of the indicator.  The latest buzz-phrase to this condition is “actionable” but that’s just a marketing ploy to make a largely esoteric and mundane evolution sound more exciting.  No indicator by itself is ever actionable.  In some cases the best action is no action.  Furthermore, a seemingly insignificant effort may have asymmetrical impacts that threaten the project itself.  This is where risk enters the picture.

When speaking of risk, all too often the discussion comes down to simulated Monte Carlo analysis.  For the project professional situated within the earned value domain, this is a convenient way to pigeonhole the concept and keep it bounded within familiar pathways, but it does little to add new information.  When applied within this context the power of Monte Carlo is limited to a range of probable outcomes within the predictive capabilities of EVM and the IMS.  This is not to minimize the importance of applying the method to these artifacts but, instead, a realization that it is a limited application.

For risk also includes factors that are external to these measurements.  Oftentimes this is called qualitative risk, but that is an all too familiar categorization that makes it seem fuzzy.  These external factors are usually the driving environment factors that limit the ability of the project to adapt.  These factors also incorporate the framing assumptions underlying the justification for the project effort.  Thus, we are led to financing and the conditions needed to achieve the next milestone for financing.  In government project management, this is known as the budget hearing cycle, and it can be both contentious and risky.

Thus, as with the title of this post, the project is really the child of the contract.  Yet when speaking of contract management the terms or often intertwined, or are relegated to the prosaic legalese of contract clauses and, in government, to the Federal Acquisition Regulation (FAR).  But that does not constitute contract management.

This is where our discussions became interesting.  Because need invoke only one element not incorporated into consideration to prove the point.  Let’s take Contract Budget Base (CBB).  This number is made up of the negotiated contract cost (NCC) plus authorized unpriced work (AUW).  In order to take these elements into account, since existing systems act as if they are external to consideration, ephemeral tools or spreadsheets are used to augment the tracking and incorporation of AUW and its impact on the CBB, though the risk of incorrectly tracking and incorporating this work is immeasurably more risky than any single work package or control account in the more closely monitored program management baseline (PMB).  The same goes with management reserve (MR), and even within the PMB itself, undistributed budget (UB), work authorizations (WADs), and change order tracking and impact analysis are often afterthoughts.

But back to the contract itself, the highest elements of the contract are the total allocated budget (TAB) and profit/fee.  But this is simply shorthand for the other elements that affect the TAB.  For example, some contracts have contract clauses that provide incentives and/or penalties that are tied to technical achievement or milestones, yet our project systems act as if these conditions are unanticipated events that fall from the sky.  Only by augmenting project management indicators are these important contract management anticipated and their impacts assessed.

In my own experience, in looking at the total contract, I have seen projects fail for want of the right “color” of money being provided within the window for decisive impact on risk manifestation.  Thus, cashflow–and the manner in which cashflow is released to fund a project–enters the picture.  But more to the point, I have seen the decision regarding cashflow made based on inadequate or partial data that was collected at a level of the structure that was largely irrelevant.  When looking at the life-cycle management of a system–another level up in our hierarchy–our need for awareness–and the information systems that can augment that awareness–becomes that much more acute.

The point here is that, while we are increasingly concerned about the number of angels dancing on the head of the EVM pin, we are ignoring other essential elements of project success.  When speaking of integrated project management, we are speaking of slightly expanding our attention span in understanding the project ecosystem–and yet even those moderate efforts meet resistance.  Given new technology, it is time to begin incorporating those elements that go well beyond the integration of cost, schedule, and bounded schedule risk.

 

Don’t Know Much…–Knowledge Discovery in Data

A short while ago I found myself in an odd venue where a question was posed about my being an educated individual, as if it were an accusation.  Yes, I replied, but then, after giving it some thought, I made some qualifications to my response.  Educated regarding what?

It seems that, despite a little more than a century of public education and widespread advanced education having been adopted in the United States, along with the resulting advent of widespread literacy, that we haven’t entirely come to grips with what it means.  For the question of being an “educated person” has its roots in an outmoded concept–an artifact of the 18th and 19th century–where education was delineated, and availability determined, by class and profession.  Perhaps this is the basis for the large strain of anti-intellectualism and science denial in the society at large.

Virtually everyone today is educated in some way.  Being “educated” means nothing–it is a throwaway question, an affectation.  The question is whether the relevant education meets the needs of the subject being addressed.  An interesting discussion about this very topic is explored at Sam Harris’ blog in the discussion he held with amateur historian Dan Carlin.

In reviewing my own education, it is obvious that there are large holes in what I understand about the world around me, some of them ridiculously (and frustratingly) prosaic.  This shouldn’t be surprising.  For even the most well-read person is ignorant about–well–virtually everything in some manner.  Wisdom is reached, I think, when you accept that there are a few things that you know for certain (or have a high probability and level of confidence in knowing), and that there are a host of things that constitute the entire library of knowledge encompassing anything from a particular domain to that of the entire universe, which you don’t know.

To sort out a well read dilettante from someone who can largely be depended upon to speak with some authority on a topic, educational institutions, trade associations, trade unions, trade schools, governmental organizations, and professional organizations have established a system of credentials.  No system is entirely perfect and I am reminded (even discounting fraud and incompetence) that half of all doctors and lawyers–two professions that have effectively insulated themselves from rigorous scrutiny and accountability to the level of almost being a protected class–graduate in the bottom half of their class.  Still, we can sort out a real brain surgeon from someone who once took a course in brain physiology when we need medical care (to borrow an example from Sam Harris in the same link above).

Furthermore, in the less potentially life-threatening disciplines we find more variation.  There are credentialed individuals who constantly get things wrong.  Among economists, for example, I am more likely to follow those who got the last financial crisis and housing market crash right (Joe Stiglitz, Dean Baker, Paul Krugman, and others), and those who have adjusted their models based on that experience (Brad DeLong, Mark Thoma, etc.), than those who have maintained an ideological conformity and continuity despite evidence.  Science–both what are called the hard and soft sciences–demands careful analysis and corroborating evidence to be tied to any assertions in their most formalized contexts.  Even well accepted theories among a profession are contingent–open to new information and discovery that may modify, append, or displace them.  Furthermore, we can find polymaths and self-taught individuals who have equaled or exceeded credentialed peers.  In the end the proof is in the pudding.

My point here is threefold.  First, in most cases we don’t know what we don’t know.  Second, complete certainty is not something that exists in this universe, except perhaps at death.  Third, we are now entering a world where new technologies allow us to discover new insights in accessing previously unavailable or previously opaque data.

One must look back at the revolution in information over the last fifty years and its resulting effect on knowledge to see what this means in our day-to-day existence.  When I was a small boy in school we largely relied on the published written word.  Books and periodicals were the major means of imparting information, aside from collocated collaborative working environments, the spoken word, and the old media of magazines, radio, and television.  Information was hard to come by–libraries were limited in their collections and there were centers of particular domain knowledge segmented by geography.   Furthermore, after the introduction of television, society had developed  trusted sources and gatekeepers to keep the cranks and flimflam out.

Today, new media–including all forms of digitized information–has expanded and accelerated the means of transmitting information.  Unlike old media, books, and social networking, there are also fewer gatekeepers in new media: editors, fact checkers, domain experts, credentialed trusted sources, etc. that ensure quality control, reliability, fidelity of the information, and provide context.  It’s the wild west of information and those wooed by the voodoo of self-organization contribute to the high risk associated with relying on information provided through these sources.  Thus, organizations and individuals who wish to stay within the fact-based community have had to sort out reliable, trusted sources and, even in these cases, develop–for lack of a better shorthand–BS detectors.  There are two purposes to this exercise: to expand the use of the available data and leverage the speed afforded by new media, and to ensure that the data is reliable and can reliably tell us something important about our subject of interest.

At the level of the enterprise, the sector, or the project management organization, we similarly are faced with the situation in which the scope of data that can be converted into information is rapidly expanding.  Unlike the larger information market, this data on the microeconomic level is more controlled.  Given that data at this level suffers from significance because it records isolated events, or small sample sizes, the challenge has been to derive importance from data where sometimes significance is minimal.

Furthermore, our business systems, because of the limitations of the selected technology, have been self-limiting.  I come across organizations all the time who cannot imagine the incorporation and integration of additional data sets largely because the limitations of their chosen software solution has inculcated that approach–that belief–into the larger corporate culture.  We do not know what we do not know.

Unfortunately, it’s what you do not know that, more often than not, will play a significant role in your organization’s destiny, just as an individual that is more self-aware is better prepared to deal with the challenges that manifest themselves as risk and its resultant probabilities.  Organizations must become more aware and look at things differently, especially since so many of the more conventional means of determining risk and opportunities seems to be failing to keep up with the times, which is governed by the capabilities of new media.

This is the imperative of applying knowledge discovery in data at the organizational and enterprise level–and in shifting one’s worldview from focusing on the limitations of “tools”: how they paint a screen, whether data is displayed across the x or y axis, what shade of blue indicates good performance, how many keystrokes does it take to perform an operation, and all manner of glorified PowerPoint minutia–to a focus on data:  the ability of solutions to incorporate more data, more efficiently, more quickly, from a wider range of sources, and processed in a more effective manner, so that it is converted into information to be able to be used to inform decision making at the most decisive moment.

The Monster Mash — Zombie Ideas in Project and Information Management

Just completed a number of meetings and discussions among thought leaders in the area of complex project management this week, and I was struck by a number of zombie ideas in project management, especially related to information, that just won’t die.  The use of the term zombie idea is usually attributed to the Nobel economist Paul Krugman from his excellent and highly engaging (as well as brutally honest) posts at the New York Times, but for those not familiar, a zombie idea is “a proposition that has been thoroughly refuted by analysis and evidence, and should be dead — but won’t stay dead because it serves a political purpose, appeals to prejudices, or both.”

The point is that to a techie–or anyone engaged in intellectual honesty–is that they are often posed in the form of question begging, that is, they advance invalid assumptions in the asking or the telling.  Most often they take the form of the assertive half of the same coin derived from “when did you stop beating your wife?”-type questions.  I’ve compiled a few of these for this post and it is important to understand the purpose for doing so.  It is not to take individuals to task or to bash non-techies–who have a valid reason to ask basic questions based on what they’ve heard–but propositions put forth by people who should know better based on their technical expertise or experience.  Furthermore, knowing and understanding technology and its economics is really essential today to anyone operating in the project management domain.

So here are a few zombies that seem to be most common:

a.  More data equals greater expense.  I dealt with this issue in more depth in a previous post, but it’s worth repeating here:  “When we inform Moore’s Law by Landauer’s Principle, that is, that the energy expended in each additional bit of computation becomes vanishingly small, it becomes clear that the difference in cost in transferring a MB of data as opposed to a KB of data is virtually TSTM (“too small to measure”).”  The real reason why we continue to deal with this assertion is both political in nature and also based in social human interaction.  People hate oversight and they hate to be micromanaged, especially to the point of disrupting the work at hand.  We see behavior, especially in regulatory and contractual relationships, where the reporting entity plays the game of “hiding the button.”  This behavior is usually justified by pointing to examples of dysfunction, particularly on the part of the checker, where information submissions lead to the abuse of discretion in oversight and management.  Needless to say, while such abuse does occur, no one has yet to point quantitatively to data (as opposed to anecdotally) that show how often this happens.

I would hazard to guess that virtually anyone with some experience has had to work for a bad boss; where every detail and nuance is microscopically interrogated to the point where it becomes hard to make progress on the task at hand.  Such individuals, who have been advanced under the Peter principle must, no doubt, be removed from such a position.  But this often happens in any organization, whether it be in private enterprise–especially in places where there is no oversight, check-and-balances, means of appeal, or accountability–or government–and is irrelevant to the assertion.  The expense item being described is bad management, not excess data.  Thus, such assertions are based on the antecedent assumption of bad management, which goes hand-in-hand with…

b. More information is the enemy of efficiency.  This is the other half of the economic argument to more data equals greater expense.  And I failed to mention that where the conflict has been engaged over these issues, some unjustifiable figure is given for the additional data that is certainly not supported by the high tech economics cited above.  Another aspect of both of these perspectives also comes from the conception of non-techies that more data and information is equivalent to pre-digital effort, especially in conceptualizing the work that often went into human-readable reports.  This is really an argument that supports the assertion that it is time to shift the focus from fixed report formatting functionality in software based on limited data to complete data, which can be formatted and processed as necessary.  If the right and sufficient information is provided up-front, then additional questions and interrogatories that demand supplemental data and information–with the attendant multiplication of data streams and data islands that truly do add cost and drive inefficiency–are at least significantly reduced, if not eliminated.

c.  Data size adds unmanageable complexity.  This was actually put forth by another software professional–and no doubt the non-techies in the room would have nodded their heads in agreement (particularly given a and b above), if opposing expert opinion hadn’t been offered.  Without putting too fine a point on it, a techie saying this to an open forum is equivalent to whining that your job is too hard.  This will get you ridiculed at development forums, where you will be viewed as an insufferable dilettante.  Digitized technology for well over 40 years has been operating under the phenomenon of Moore’s Law.  Under this law, computational and media storage capability doubles at least every two years under the original definition, though that equation has accelerated to somewhere between 12 and 24 months.  Thus, what was considered big data, say, in 1997 when NASA first coined the term, is not considered big data today.  No doubt, what is considered big data this year will not be considered big data two years from now.  Thus, the term itself is relative and may very well become archaic.  The manner in which data is managed–its rationalization and normalization–is important in successfully translating disparate data sources, but the assertion that big is scary is simply fear mongering because you don’t have the goods.

d.  Big data requires more expensive and sophisticated approaches.  This flows from item c above as well and is often self-serving.  Scare stories abound, often using big numbers which sound scary.  All data that has a common use across domains has to be rationalized at some point if they come from disparate sources, and there are a number of efficient software techniques for accomplishing this.  Furthermore, support for agnostic APIs and common industry standards, such as the UN/CEFACT XML, take much of the rationalization and normalization work out of a manual process.  Yet I have consistently seen suboptimized methods being put forth that essentially require an army of data scientists and coders to essentially engage in brute force data mining–a methodology that has been around for almost 30 years: except that now it carries with it the moniker of big data.  Needless to say this approach is probably the most expensive and slowest out there.  But then, the motivation for its use by IT shops is usually based in rice bowl and resource politics.  This is flimflam–an attempt to revive an old zombie under a new name.  When faced with such assertions, see Moore’s Law and keep on looking for the right answer.  It’s out there.

e.  Performance management and assessment is an unnecessary “regulatory” expense.  This one keeps coming up as part of a broader political agenda beyond just project management.  I’ve discussed in detail the issues of materiality and prescriptiveness in regulatory regimes here and here, and have addressed the obvious legitmacy of organizations to establish one in fiduciary, contractual, and governmental environments.

My usual response to the assertion of expense is to simply point to the unregulated derivatives market largely responsible for the financial collapse, and the resulting deep economic recession that followed once the housing bubble burst.  (And, aside from the cost of human suffering and joblessness, the expenses related to TARP).  Thus we know that the deregulation of banking had gone so well.  Even after the Band-Aid of Dodd-Frank the situation probably requires a bit more vigor, and should include the ratings agencies as well as the real estate market.  But here is the fact of the matter: such expenses cannot be monetized as additive because “regulatory” expenses usually represent an assessment of the day-to-day documentation, systems, and procedures required when performing normal business operations and due diligence in management.  I attended an excellent presentation last week where the speaker, tasked with finding unnecessary regulatory expenses, admitted as much.

Thus, what we are really talking about is an expense that is an essential prerequisite to entry in a particular vertical, especially where monopsony exists as a result of government action.  Moral hazard, then, is defined by the inherent risk assumed by contract type, and should be assessed on those terms.  Given the current trend is to raise thresholds, the question is going to be–in the government sphere–whether public opinion will be as forgiving in a situation where moral hazard assumes $100M in risk when things head south, as they often do with regularity in project management.  The way to reduce that moral hazard is through sufficiency of submitted data.  Thus, we return to my points in a and b above.

f.  Effective project assessment can be performed using high level data.  It appears that this view has its origins in both self-interest and a type of anti-intellectualism/anti-empiricism.

In the former case, the bias is usually based on the limitations of either individuals or the selected technology in providing sufficient information.  In the latter case, the argument results in a tautology that reinforces the fallacy that absence of evidence proves evidence of absence.  Here is how I have heard the justification for this assertion: identifying emerging trends in a project does not require that either trending or lower level data be assessed.  The projects in question are very high dollar value, complex projects.

Yes, I have represented this view correctly.  Aside from questions of competency, I think the fallacy here is self-evident.  Study after study (sadly not all online, but performed within OSD at PARCA and IDA over the last three years) have demonstrated that high level data averages out and masks indicators of risk manifestation, which could have been detected looking at data at the appropriate level, which is the intersection of work and assigned resources.  In plain language, this requires integration of the cost and schedule systems, with risk first being noted through consecutive schedule performance slips.  When combined with technical performance measures, and effective identification of qualitative and quantitative risk tied to schedule activities, the early warning is two to three months (and sometime more) before the risk is reflected in the cost measurement systems.  You’re not going to do this with an Excel spreadsheet.  But, for reference, see my post  Excel is not a Project Management Solution.

It’s time to kill the zombies with facts–and to behead them once and for all.

Second Foundation — More on a General Theory of Project Management

In ending my last post on developing a general theory of project management, I introduced the concept of complex adaptive systems (CAS) and posited that projects and their ecosystems fall into this specific category of systems theory.  I also posited that it is through the tools of CAS that we will gain insight into the behavior of projects.  The purpose is not only to identify commonalities in these systems across what is frequently asserted are irreconcilable across economic market verticals, but to identify regularities and the proper math in determining the behavior of these systems.

A brief overview of some of the literature is in order so that we can define our terms, since CAS is a Protean term that has evolved with its application.  Aside from the essential work at the Santa Fe Institute, some of which I linked in my last post on the topic, I would first draw your attention to an overview of CAS by Serena Chan at MIT.  Ms. Chan wrote her paper in 2001, and so her perspective in one important way has proven to be limited, which I will shortly address.  Ms. Chan correctly defines complexity and I will leave it to the reader to go to the link above to read the paper.  The meat of her paper is her definition of CAS by identifying its characteristics.  These are: distributed control, connectivity, co-evolution, sensitive dependence on initial conditions, emergence, distance from equilibrium, and existence in a state of paradox.  She then posits some tools that may be useful in studying the behavior of CAS and then concludes with an odd section on the application of CAS to engineering systems, positing that engineering systems cannot be CAS because they are centrally controlled and hence do not exhibit emergence (non-preprogrammed behavior).  She interestingly uses the example of the internet as her proof.  In the year 2015, I don’t think one can seriously make this claim.  Even in 2001 such an assertion would be specious for it had been ten years since the passage of the High Performance Computing and Communication Act of 1991 (also called the Gore Bill) which commercialized ARPANET.  (Yes, he really did have a major hand in “inventing” the internet as we know it).  It was also eight years from the introduction of Mosaic.  Thus, the internet, as many engineering systems requiring collaboration and human interaction, fall under the rubric of CAS as defined by Ms. Chan.

The independent consultant Peter Fryer at his Trojan Mice blog adds a slightly different spin to identifying CAS.  He asserts that CAS properties are emergence, co-evolution, suboptimal, requisite variety, connectivity, simple rules, iteration, self-organizing, edge of chaos, and nested systems.  My only pique with many of these stated characteristics is that they seem to be slightly overlapping and redundant, splitting hairs without adding to our understanding.  They also tend to be covered by the larger definitions of systems theory and complexity.  Perhaps its worth reducing them within CAS because they provide specific avenues in which to study these types of systems.  We’ll explore this in future posts.

An extremely useful book on CAS is by John H. Miller and Scott E. Page under the rubric of the Princeton Studies in Complexity entitled Complex Adaptive Systems: An Introduction to Computational Models of Social Life.  I strongly recommend it.  In the book Miller and Page explore the concepts of emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback in CAS.  They also recommend mathematical models to study and assess the behavior of CAS.  In future posts I will address the limitations of mathematics and its inability to contribute to learning, as opposed to providing logical proofs of observed behavior.  Needless to say, this critique will also discuss the further limitations of statistics.

Still, given these stated characteristics, can we state categorically that a project organization is a complex adaptive system?  After, all people attempt to control the environment, there are control systems in place, oftentimes work and organizations are organized according to the expenditure of resources, there is a great deal of planning, and feedback occurs on a regular basis.  Is there really emergence and diversity in this kind of environment?  I think so.  The reason why I think so is because of the one obvious factor that is measures despite the best efforts to exert control, which in reality consists of multiple agents: the presence of risk.  We think we have control of our projects, but in reality we only can exert so much control. Oftentimes we move the goalposts to define success.  This is not necessarily a form of cheating, though sometimes it can be viewed in that context.  The goalposts change because in human CAS we deal with the concept of recursion and its effects.  Risk and recursion are sufficient to land project efforts clearly within the category of CAS.  Furthermore, that projects clearly fall within the definition of CAS follows below.

It is within an extremely useful paper written on CAS from a practical standpoint that was published in 2011 and written by Keith L. Green of the Institute for Defense Analysis (IDA) entitled Complex Adaptive Systems in Military Analysis that we find a clear and comprehensive definition.  In borrowing from A. S. Elgazzar, of both the mathematics departments of El-Arish, Egypt and Al-Jouf King Saud University in the Kingdom of Saudi Arabia; and A. S. Hegazi of the Mathematics Department, Faculty of Science at Mansoura, Egypt–both of whom have contributed a great deal of work on the study of the biological immune systems as a complex adaptive system–Mr. Green states:

A complex adaptive system consists of inhomogeneous, interacting adaptive agents.  Adaptive means capable of learning.  In this instance, the ability to learn does not necessarily imply awareness on the part of the learner; only that the system has memory that affects its behavior in the environment.  In addition to this abstract definition, complex adaptive systems are recognized by their unusual properties, and these properties are part of their signature.  Complex adaptive systems all exhibit non-linear, unpredictable, emergent behavior.  They are self-organizing in that their global structures arise from interactions among their constituent elements, often referred to as agents.  An agent is a discrete entity that behaves in a given manner within its environment.  In most models or analytical treatments, agents are limited to a simple set of rules that guide their responses to the environment.  Agents may also have memory or be capable of transitioning among many possible internal states as a consequence of their previous interactions with other agents and their environment.  The agents of the human brain, or of any brain in fact, are called neurons, for example.  Rather than being centrally controlled, control over the coherent structure is distributed as an emergent property of the interacting agents.  Collectively, the relationships among agents and their current states represent the state of the entire complex adaptive system.

No doubt, this definition can be viewed as having a specific biological bias.  But when applied to the artifacts and structures of more complex biological agents–in our case people–we can clearly see that the tools we use must been broader than those focused on a specific subsystem that possesses the attributes of CAS.  It calls for an interdisciplinary approach that utilizes not only mathematics, statistics, and networks, but also insights from the areas of the physical and computational sciences, economics, evolutionary biology, neuroscience, and psychology.  In understanding the artifacts of human endeavor we must be able to overcome recursion in our observations.  It is relatively easy for an entomologist to understand the structures of ant and termite colonies–and the insights they provide of social insects.  It has been harder, particularly in economics and sociology, for the scientific method to be applied in a similarly detached and rigorous method.  One need only look to the perverse examples of Spencer’s Social Statics and Murray and Herrnstein’s The Bell Curve as but two examples where selection bias, ideology, class bias, and racism have colored such attempts regarding more significant issues.

It is my intent to avoid bias by focusing on the specific workings of what we call project systems.  My next posts on the topic will focus on each of the signatures of CAS and the elements of project systems that fall within them.

Talking (Project Systems) Blues: A Foundation for a General Theory

As with those of you who observe the upcoming Thanksgiving holiday, I find myself suddenly in a state of non-motion and, as a result, with feet firmly on the ground, able to write a post.  This is preface to pointing out that the last couple of weeks have been both busy and productive in a positive way.

Among the events of the last two weeks was the meeting of project management professionals focused on the discipline of aerospace and defense at the Integrated Program Management Workshop.  This vertical, unlike other areas of project management, is characterized by applying a highly structured approach that involves a great deal of standardization.  Most often, people involved in this area tend to engage in an area where the public sector plays a strong role in defining the environment in which the market operates.  Furthermore, the major suppliers tend to be limited, and so both oligopolistic and monopolistic market competition defines the market space.

Within this larger framework, however, is a set of mid-level and small firms engaged in intense competition to provide both supplies and services to the limited set of large suppliers.  As such, they operate within the general framework of the larger environment defined by public sector procedures, laws, and systems, but within those constraints act with a great deal of freedom, especially in acting as a conduit to commercial and innovative developments from the private sector.

Furthermore, since many technologies originate within the public sector (as in the internet, microchips, etc. among other examples since the middle of the 20th century), the layer of major suppliers, and mid-level to small businesses also act as a conduit to introducing such technologies to the larger private sector.  Thus, the relationship is a mutually reinforcing one.

Given the nature of this vertical and its various actors, I’ve come upon the common refrain that it is unique in its characteristics and, as such, acts as a poor analogue of other project management systems.  Dave Gordon, for example, who is a well-respected expert in IT projects in commenting on previous posts, has expressed some skepticism in my suggestion that there may be commonalities across the project management discipline regardless of vertical or end-item development.  I have promised a response and a dialogue and, given recent discussions, I think I have a path forward.

I would argue, instead, that the nature of the aerospace and defense (A&D) vertical provides a perfect control for determining the strength of commonalities.  My contention is that because larger and less structured economic verticals do not have the same ability to control the market environment and mechanisms that they provide barriers to identifying possible commonalities due to their largely chaotic condition.  Thus, unlike in other social sciences, we are not left with real time experimentation absent a control group.  Both non-A&D and A&D verticals provide the basis to provide controls for the other, given enough precision in identifying the characteristics being identified and measured.

But we need a basis, a framework for identifying commonalities.  As such our answers will be found in systems theory.  This is not a unique or new observation, but for the basis of outlining our structure it is useful to state the basis of the approach.  For those of you playing along at home, the seminal works in this area are Norbert Weiner’s Cybernetics or, Control and Communication in the Animal and the Machine (1948), and Ludwig von Bertalanffy’s General Systems Theory (1968).

But we must go beyond basic systems theory in its formative stage.  Project are a particular type of system, a complex system.  Even beyond that they must go one more step, because they are human systems that both individually in its parts and in aggregate displays learning.  As such these are complex adaptive systems or CAS.  They exist in a deterministic universe, as all CAS do, but are non-deterministic within the general boundaries of that larger physical world.

The main thought leaders of CAS are John H. Holland, as in this 1992 paper in Daedalus, and Murray Gell-Mann with his work at the Santa Fe Institute.  The literature is extensive and this is just the start, including taking into account the work of Kristo Ivanov from the concepts coming out of his work, Hypersystems: A Base for Specification of Computer-Supported Self-Learning Social Systems.

It is upon this basis, especially in the manner in which the behavior that CAS can be traced and predicted, where will be able to establish the foundation of a general theory of project management systems.  I’ll be vetting ideas over the coming weeks regarding this approach, with some suggestions on real world applicability and methodologies across project domains.

Walk This Way — DoD IG Reviews DCMA Contracting Officer Business Systems Deficiencies

The sufficiency and effectiveness of business systems is an essential element in the project management ecosystem.  Far beyond performance measurement of the actual effort, the sufficiency of the business systems to support the effort are essential in its success.  If the systems in place do not properly track and record the transactions behind the work being performed, the credibility of the data is called into question.  Furthermore, support and logistical systems, such as procurement, supply, and material management, contribute in a very real way, to work accomplishment.  If that spare part isn’t in-house on time, the work stops.

In catching up on reading this month, I found that the DoD Inspector General issued a report on October 1 showing that of 21 audits demonstrating business system deficiencies, contracting officer timeliness in meeting DFARS deadlines at various milestones existed in every case.  For example, in 17 of those cases Contracting Officers did not issue final determination letters within 30 days of the report as required by the DFARS.  In eight cases required withholds were not assessed.

For those of you who are unfamiliar with the six business systems assessed under DoD contractor project management, they consist of accounting, estimating, material management, purchasing, earned value management, and government property.  The greater the credibility and fidelity of these systems, the greater level of confidence that the government can have in ensuring that the data received in reporting on execution of public funds under these contracts.

To a certain extent the deadlines under the DFARS are so tightly scheduled that they fail to take into account normal delays in operations.  Forbid that the Contracting Officer may be on leave when the audit is received or is engaged in other detailed negotiations.  In recent years the contracting specialty within the government, like government in general, has been seriously understaffed, underfunded, and unsupported.  Given that oftentimes the best and the brightest soon leave government service for greener pastures in the private sector, what is often left are inexperienced and overworked (though mostly dedicated) personnel who do not have the skills or the time to engage in systems thinking in approaching noted deficiencies in these systems.

This pressure for staff reduction, even in areas that have been decimated by austerity politics, is significant.  In the report I could not help but shake my head when an Excel spreadsheet was identified as the “Contractor Business System Determination Timeline Tracking Tool.”  This reminds me of my initial assignment as a young Navy officer and my first assignment as a contract negotiator where I also performed collateral duties in building simple automated tools.  (This led to me being assigned later as the program manager of the first Navy contract and purchase order management system.) That very first system that I built, however, was tracking contract milestone deadlines.  It was done in VisiCalc and the year was 1984.

That a major procurement agency of the U.S. Department of Defense is still using a simple and ineffective spreadsheet tracking “tool” more than 30 years after my own experience is both depressing and alarming.  There is a long and winding history on why they would find themselves in this condition, but some additional training, which was the agency’s response to the IG, is not going to solve the problem.  In fact, such an approach is so ineffective it’s not even a Band-Aid.  It’s a bureaucratic function of answering the mail.

The reason why it won’t solve the problem is because there is no magic wand to get those additional contract negotiators and contracting officers in place.  The large intern program of recruiting young people from colleges to grow talent and provide people with a promising career track is long gone.  Interdisciplinary and cross-domain expertise required in today’s world to reflect the new realities when procuring products and services are not in the works.  In places where they are being attempted, outmoded personnel classification systems based on older concepts of division of labor stand in the way.

The list of systemic causes could go on, but in the end it’s not in the DCMA response because no one cares, and if they do care, they can’t do anything about it.  It’s not as if “BEST TALENT LEAVES DUE TO PUBLIC HOSTILITY TO PUBLIC SERVICE”  was a headline of any significance.  The Post under Bezos is not going to run that one anytime soon, though we’ve been living under it since 1981.  The old “thank you for your service” line for veterans has become a joke.  Those who use this line might as well say what that really means, which is: “I’m glad it was you and not me.”

The only realistic way to augment an organization in this state in order the break the cycle is to automate the system–and to do it in a way as to tie together the entire system.  When I run into my consulting friends and colleagues and they repeat the mantra: “software doesn’t matter, it’s all based on systems” I can only shake my head.  I have learned to be more tactful.

In today’s world software matters.  Try doing today what we used to do with slide rules, scientific calculators, and process charts absent software.  Compare organizations that use the old division-of-labor, “best of breed” tool concept against those who have integrated their systems and use data across domains effectively.  Now tell me again why “software doesn’t matter.”  Not only does it matter but “software” isn’t all the same.  Some “software” consists of individual apps that do one thing.  Some “software” is designed to address enterprise challenges.  Some “software” is designed not only to enterprise challenges, but also to address the maximization of value in enterprise data.

In the case of procurement and business systems assessment, the only path forward for the agency will be to apply data-driven measures to the underlying systems and tie those assessments into a systemic solution that includes the contracting officers, negotiators, administrators, contracting officer representatives, the auditors, analysts, and management.  One can see, just in writing one line, how much more complex are the requirements for the automated panacea to replace “Contractor Business System Determination Timeline Tracking Tool.”  Is there any question why the “tool” is ineffective?

If this were the 1990s, though the practice still persists, we would sit down, perform systems analysis, outline the systems and subsystem solutions, and then through various stages of project management, design the software system to reflect the actual system in place as if organizational change did not exist.  This is the process that has a 90% failure rate across government and industry.  The level of denial to this figure is so great that I run into IT managers and CIOs every day that fail to know it or, if they do, believe that it will apply to them–and these are brilliant people.  It is selection bias and optimism, with a little (or a lot) of narcissism, run amok.  The physics and math on this are so well documented that you might as well take your organization’s money and go to Vegas with it.  Your local bookie could give you better odds.

The key is risk handling (not the weasel word “management,” not “mitigation” since some risks must simply be accepted, and certainly not the unrealistic term “avoidance”), and the deployment of technology that provides at least a partial solution to the entire problem, augmented by incremental changes to incorporate each system into the overall solution. For example, DeLong and Froomkin’s seminal paper on what they called “The Next Economy” holds true today.  The lack of transparency in software technologies requires a process whereby the market is surveyed, vendors must go through a series of assessments and demonstration tests, and where the selected technology then goes through stage gates: proof-of-concept, pilot, and, eventually deployment.  Success at each level gets rewarded with proceeding to the next step.

Thus, ideally the process includes introducing into the underlying functionality the specific functionality required by the organization through Agile processes where releasable versions of the solution are delivered at the end of each sprint.  One need not be an Agile Cultist to do this.  In my previous post I referred to Neil Killick’s simple checklist for whether you are engaged in Agile.  It is the best and most succinct distillation of both the process and value inherent in Agile that I have found to date, with all of the “woo-woo” taken out.  For an agency as Byzantine as DCMA, this is really the only realistic and effective approach.

DCMA is an essential agency in DoD acquisition management, but it cannot do what it once did under a more favorable funding environment.  To be frank, it didn’t even do its job all that well when a more favorable condition was in place, though things were better.  But this is also a factor in why it finds itself in its current state.  It was punished for its transgressions, perhaps too much.  Several waves of personnel cuts, staff reductions, and domain and corporate knowledge loss on top of the general trend has created an agency in a condition of siege.  As with any organization under siege, backbiting and careerism for those few remaining is rewarded.  Iconoclasts and thought leaders stay for a while before being driven away.  They are seen as being too risky.

This does not create a condition for an agency ready to accept or quickly execute change through new technology.  What it does do is allow portions of the agency to engage in cargo cult change management.  That is, it has the appearance of change but keeps self-interest comfortable and change in its place.  Over time–several years–with the few remaining resources committed to this process, they will work the “change.”  Eventually, they may even get something tangible, though suboptimized to conform to rice bowls; preferably after management has their retirement plans secured.

Still, the reality is that DCMA must be made to do it’s job because it is in the best interests of the U.S. Department of Defense.  The panacea will not be found through “collaboration” with industry, which consists of the companies which DCMA is tasked with overseeing and regulating.  We all know how well deregulation and collaboration has worked in the financial derivatives, banking, mortgage, and stock markets.  Nor will it come from organic efforts within an understaffed and under-resourced agency that will be unable to leverage the best and latest technology solutions under the unforgiving math of organic IT failure rates.  Nor will deploying the long outmoded approach of deploying suboptimized “tools” to address a particular problem.  The proper solution is to leverage effective COTS solutions that facilitate the challenge of systems integration and thinking.

 

 

Do You Believe in Magic? — Big Data, Buzz Phrases, and Keeping Feet Planted Firmly on the Ground

My alternative title for this post was “Money for Nothing,” which is along the same lines.  I have been engaged in discussions regarding Big Data, which has become a bit of a buzz phrase of late in both business and government.  Under the current drive to maximize the value of existing data, every data source, stream, lake, and repository (and the list goes on) has been subsumed by this concept.  So, at the risk of being a killjoy, let me point out that not all large collections of data is “Big Data.”  Furthermore, once a category of data gets tagged as Big Data, the further one seems to depart from the world of reality in determining how to approach and use the data.  So for of you who find yourself in this situation, let’s take a collective deep breath and engage our critical thinking skills.

So what exactly is Big Data?  Quite simply, as noted by this article in Forbes by Gil Press, term is a relative one, but generally means from a McKinsey study, “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.”  This subjective definition is a purposeful one, since Moore’s Law tends to change what is viewed as simply digital data as opposed to big data.  I would add some characteristics to assist in defining the term based on present challenges.  Big data at first approach tends to be unstructured, variable in format, and does not adhere to a schema.  Thus, not only is size a criteria for the definition, but also the chaotic nature of the data that makes it hard to approach.  For once we find a standard means of normalizing, rationalizing, or converting digital data, it no longer is beyond the ability of standard database tools to effectively use it.  Furthermore, the very process of taming it thereby renders it non-big data, or perhaps, if a exceedingly large dataset, perhaps “small big data.”

Thus, having defined our terms and the attributes of the challenge we are engaging, we now can eliminate many of the suppositions that are floating around in organizations.  For example, there is a meme that I have come upon that asserts that disparate application file data can simply be broken down into its elements and placed into database tables for easy access by analytical solutions to derive useful metrics.  This is true in some ways but both wrong and dangerous in its apparent simplicity.  For there are many steps missing in this process.

Let’s take, for example, the least complex example in the use of structured data submitted as proprietary files.  On its surface this is an easy challenge to solve.  Once someone begins breaking the data into its constituent parts, however, greater complexity is found, since the indexing inherent to data interrelationships and structures are necessary for its effective use.  Furthermore, there will be corruption and non-standard use of user-defined and custom fields, especially in data that has not undergone domain scrutiny.  The originating third-party software is pre-wired to be able to extract this data properly.  Absent having to use and learn multiple proprietary applications with their concomitant idiosyncrasies, issues of sustainability, and overhead, such a multivariate approach defeats the goal of establishing a data repository in the first place by keeping the data in silos, preventing integration.  The indexing across, say, financial systems or planning systems are different.  So how do we solve this issue?

In approaching big data, or small big data, or datasets from disparate sources, the core concept in realizing return on investment and finding new insights, is known as Knowledge Discovery in Databases or KDD.  This was all the rage about 20 years ago, but its tenets are solid and proven and have evolved with advances in technology.  Back then, the means of extracting KDD from existing databases was the use of data mining.

The necessary first step in the data mining approach is pre-processing of data.  That is, once you get the data into tables it is all flat.  Every piece of data is the same–it is all noise.  We must add significance and structure to that data.  Keep in mind that we live in this universe, so there is a cost to every effort known as entropy.  Computing is as close as you’ll get to defeating entropy, but only because it has shifted the burden somewhere else.  For large datasets it is pushed to pre-processing, either manual or automated.  In the brute force world of data mining, we hire data scientists to pre-process the data, find commonalities, and index it.  So let’s review this “automated” process.  We take a lot of data and then add a labor-intensive manual effort to it in order to derive KDD.  Hmmm..  There may be ROI there, or there may not be.

But twenty years is a long time and we do have alternatives, especially in using Fourth Generation software that is focused on data usage without the limitations of hard-coded “tools.”  These alternatives apply when using data on existing databases, even disparate databases, or file data structured under a schema with well-defined data exchange instructions that allow for a consistent manner of posting that data to database tables. The approach in this case is to use APIs.  The API, like OLE DB or the older ODBC, can be used to read and leverage the relative indexing of the data.  It will still require some code to point it in the right place and “tell” the solution how to use and structure the data, and its interrelationship to everything else.  But at least we have a means for reducing the cost associated with pre-processing.  Note that we are, in effect, still pre-processing data.  We just let the CPU do the grunt work for us, oftentimes very quickly, while giving us control over the decision of relative significance.

So now let’s take the meme that I described above and add greater complexity to it.  You have all kinds of data coming into the stream in all kinds of formats including specialized XML, open, black-boxed data, and closed proprietary files.  This data is non-structured.  It is then processed and “dumped” into a non-relational database such as NoSQL.  How do we approach this data?  The answer has been to return to a hybrid of pre-processing, data mining, and the use of APIs.  But note that there is no silver bullet here.  These efforts are long-term and extremely labor intensive at this point.  There is no magic.  I have heard time and again from decision makers the question: “why can’t we just dump the data into a database to solve all our problems?”  No, you can’t, unless you’re ready for a significant programmatic investment in data scientists, database engineers, and other IT personnel.  At the end, what they deploy, when it gets deployed, may very well be obsolete and have wasted a good deal of money.

So, once again, what are the proper alternatives?  In my experience we need to get back to first principles.  Each business and industry has commonalities that transcend proprietary software limitations by virtue of the professions and disciplines that comprise them.  Thus, it is domain expertise to the specific business that drives the solution.  For example, in program and project management (you knew I was going to come back there) a schedule is a schedule, EVM is EVM, financial management is financial management.

Software manufacturers will, apart from issues regarding relative ease of use, scalability, flexibility, and functionality, attempt to defend their space by establishing proprietary lexicons and data structures.  Not being open, while not serving the needs of customers, helps incumbents avoid disruption from new entries.  But there often comes a time when it is apparent that these proprietary definitions are only euphemisms for a well-understood concept in a discipline or profession.  Cat = Feline.  Dog = Canine.

For a cohesive and well-defined industry the solution is to make all data within particular domains open.  This is accomplished through the acceptance and establishment of a standard schema.  For less cohesive industries, but where the data or incumbents through the use of common principles have essentially created a de facto schema, APIs are the way to extract this data for use in analytics.  This approach has been applied on a broader basis for the incorporation of machine data and signatures in social networks.  For closed or black-boxed data, the business or industry will need to execute gap analysis in order to decide if database access to such legacy data is truly essential to its business, or given specification for a more open standard from “time-now” will eventually work out suboptimization in data.

Most important of all and in the end, our results must provide metrics and visualizations that can be understood, are valid, important, material, and be right.